Using big data to identify triple-negative breast, oropharyngeal, and lung cancers

March 18, 2014

Researchers at Case Western Reserve University and colleagues used "big data" analytics to predict if a patient is suffering from aggressive triple-negative breast cancer, slower-moving cancers or non-cancerous lesions with 95 percent accuracy.

If the tiny patterns they found in magnetic resonance images prove consistent in further studies, the technique may enable doctors to use an MRI scan to diagnose more aggressive cancers earlier and fast track these patients for therapy. Their work is published online in the journal Radiology.

The work comes just two months after senior author Anant Madabhushi and another group of researchers showed they can detect differences between persistent and treatable forms of head and neck cancers caused by exposure to human papillomavirus, with 87.5 percent accuracy. In that study, digital images were made from slides of patients' tumors.

Next up, Madabhushi's lab recently received a $534,000, 2-year grant from the Department of Defense to find the patterns of indolent versus aggressive cancer in the lungs. The goal is to diagnose the presence of aggressive lung cancers from CT scans alone.

"Literally, what we're trying to do is squeeze out the information we're not able to see just by looking at an image," said Madabhushi, a professor of biomedical engineering at Case School of Engineering and director of the Center for Computational Imaging and Personalized Diagnostics.

Searching for telltale markers

Madabhushi worked with Shannon C. Agner at Rutgers University and Mark A. Rosen, MD; Sarah Englander; Mitchell D. Schnall, MD; Michael D. Feldman, MD; Paul Zhang, MD; and Carolyn Miles; MD, at the University of Pennsylvania, on the breast cancer study.

They analyzed MR images of breast lesions from 65 women. The researchers sifted through hundreds of gigabytes of image data from each patient to try to find differences that distinguish the different subtypes of breast cancers from each other.

Madabhushi and his colleagues discovered that triple-negative cancer, benign fibroadenoma that is commonly mistaken for triple-negative, and two other common types of —estrogen-receptor positive (ER+) and human epidermal growth factor receptor type 2-postive (HER2+)—reflect different textures when images are enhanced with contrasting agents.

The scientists mathematically modeled the textures that appear as the tissues absorb contrast-enhancing dye. The model revealed that changes over just milliseconds distinguished triple-negative from benign lesions. The investigators used machine learning and pattern recognition methods to aid in diagnoses among the three types of cancers based on texture changes and other quantitative evidence.

"Today, if a woman or her doctor finds a lump, she gets a mammogram and then a biopsy for molecular analysis, which can take two weeks or up to a month," Madabhushi said. "If we can predict the cancer is triple-negative, we can fast track the patient for biopsy and treatment. Especially in cases with triple-negative cancer, two to four weeks saved can be crucial."

For the three types of cancers, the early diagnosis would enable quick and personalized treatments. ER+ and HER2+ respond to different therapies. An MRI could also become a regular screening device for women who have family histories of these cancers.

Other cancers

Using much the same science, Madabhushi and fellow researchers from Washington University developed a way to distinguish between recurrent and treatable forms of a specific head and neck cancer called human papillomavirus-related oropharyngeal squamous cell carcinoma. That work was published earlier this year in the American Journal of Surgical Pathology. The abstract can be found at http://www.ncbi.nlm.nih.gov/pubmed/24145650.

"Most sufferers tend to have good outcomes, but a small subset—about 10 percent—doesn't," he said. "There's nothing out there to predict which.

"We developed an algorithm and found patterns that allowed us to distinguish between the two with 80 to 90 percent accuracy."

After scanning biopsy and tumor resection slides from 160 patients into a computer, the researchers found they could use nuclei of the cancerous cells to characterize and measure cell distribution and clustering patterns.

They found where the nuclei of cells had reverted to a more primitive form, a condition called anaplasia, the cells were tightly clustered and the patient suffered recurrent cancer. They graphed the nuclei in each of the images and found there was little to no overlap between the highly clustered recurrent cancer and the comparatively disperse treatable form.

The results, if confirmed through further studies, could lead to milder treatment for patients who have the non-recurrent cancer and more aggressive treatment for those with recurrent cancer, the researchers say.

"Personalized medicine is possible using this," Madabhushi said. "Using biopsy specimens, pathologists can't tell one from the other, but big data analytics can."

His lab's newest project is to find characteristics that can identify or precancerous conditions in the lungs, and distinguish among different types of lung cancers.

The majority of lung cancers are diagnosed at advanced stages, beyond the period in which surgery can be successful. Survival rate for one of the worst forms, non small-cell , remains at 15 to 18 percent. In this study, the lab will use x-ray images taken with computed tomography scans to build their digital image library.

Explore further: New finding points to potential options for attacking stem cells in triple-negative breast cancer

More information: Paper: http://pubs.rsna.org/doi/full/10.1148/radiol.14131384

Related Stories

New finding points to potential options for attacking stem cells in triple-negative breast cancer

February 17, 2014
New research from the University of Michigan Comprehensive Cancer Center and Georgia Regents University finds that a protein that fuels an inflammatory pathway does not turn off in breast cancer, resulting in an increase ...

Smoking linked with increased risk of most common type of breast cancer

February 10, 2014
Young women who smoke and have been smoking a pack a day for a decade or more have a significantly increased risk of developing the most common type of breast cancer. That is the finding of an analysis published early online ...

Aggressive breast cancers may be sensitive to drugs clogging their waste disposal

August 12, 2013
In a new paper in Cancer Cell, a team led by Judy Lieberman, PhD, of Boston Children's Hospital's Program in Cellular and Molecular Medicine reports "triple-negative" breast cancers may be vulnerable to drugs that attack ...

Additional drug shows promise for women with triple-negative breast cancer

December 13, 2013
In a nationwide study of women with "triple-negative" breast cancer, adding the chemotherapy drug carboplatin or the angiogenesis inhibitor Avastin to standard chemotherapy drugs brought a sharp increase in the number of ...

Osteoporosis drug may treat breast and liver cancers

February 19, 2014
(Medical Xpress)—A drug used to prevent and treat osteoporosis in post-menopausal women may also be able to treat some breast and liver cancers, according to a new study from Oregon State University.

Researchers discover new hormone receptors to target when treating breast cancer

February 4, 2014
According to the United States Centers for Disease Control and Prevention, breast cancer is the most common cancer in women. For patients whose breast cancers are hormone-dependent, current treatment focuses on using drugs ...

Recommended for you

Anti-cancer chemotherapeutic agent inhibits glioblastoma growth and radiation resistance

July 24, 2017
Glioblastoma is a primary brain tumor with dismal survival rates, even after treatment with surgery, chemotherapy and radiation. A small subpopulation of tumor cells—glioma stem cells—is responsible for glioblastoma's ...

New therapeutic approach for difficult-to-treat subtype of ovarian cancer identified

July 24, 2017
A potential new therapeutic strategy for a difficult-to-treat form of ovarian cancer has been discovered by Wistar scientists. The findings were published online in Nature Cell Biology.

Immune cells the missing ingredient in new bladder cancer treatment

July 24, 2017
New research offers a possible explanation for why a new type of cancer treatment hasn't been working as expected against bladder cancer.

Shooting the achilles heel of nervous system cancers

July 20, 2017
Virtually all cancer treatments used today also damage normal cells, causing the toxic side effects associated with cancer treatment. A cooperative research team led by researchers at Dartmouth's Norris Cotton Cancer Center ...

Molecular changes with age in normal breast tissue are linked to cancer-related changes

July 20, 2017
Several known factors are associated with a higher risk of breast cancer including increasing age, being overweight after menopause, alcohol intake, and family history. However, the underlying biologic mechanisms through ...

Immune-cell numbers predict response to combination immunotherapy in melanoma

July 20, 2017
Whether a melanoma patient will better respond to a single immunotherapy drug or two in combination depends on the abundance of certain white blood cells within their tumors, according to a new study conducted by UC San Francisco ...

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.